Neil D. B. Bruce

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A model of bottom-up overt attention is proposed based on the principle of maximizing information sampled from a scene. The proposed operation is based on Shannon's self-information measure and is achieved in a neural circuit, which is demonstrated as having close ties with the circuitry existent in the primate visual cortex. It is further shown that the(More)
A proposal for saliency computation within the visual cortex is put forth based on the premise that localized saliency computation serves to maximize information sampled from one's environment. The model is built entirely on computational constraints but nevertheless results in an architecture with cells and connectivity reminiscent of that appearing in the(More)
The necessity and utility of visual attention are discussed in the context of stereo vision in machines and primates. Specific problems that arise in this domain including binocular rivalry, and the deployment of attention in three-dimensional space are considered. Necessary conditions are outlined for achieving appropriate attentional behaviour in both the(More)
A novel image operator is proposed for the purpose of predicting the focus of visual attention in arbitrary natural scenes based on local statistics. The proposed method is based on the hypothetical premise that attention proceeds by way of sampling a scene in a manner that maximizes the information acquired from the scene. A tractable means of computing(More)
In this paper, we present a solution to the problem of dynamic range compression from multiple exposures called ExpoBlend that operates in the absence of raw format images, relative or absolute exposure values, camera response functions, or known irradiance. This is achieved in relatively simplistic fashion by merging image content across provided(More)
An object may afford a number of different actions. In this article, we show that an attentional mechanism inhibits competing motor programs that could elicit erroneous actions. Participants made a speeded key press to categorize the second of two successively presented door handles that were rotated at varying orientations relative to one another. Their(More)
In the past decade, a large number of computational models of visual saliency have been proposed. Recently a number of comprehensive benchmark studies have been presented, with the goal of assessing the performance landscape of saliency models under varying conditions. This has been accomplished by considering fixation data, annotated image regions, and(More)
The human brain uses visual attention to facilitate object recognition. Traditional theories and models envision this attentional mechanism either in a pure feedforward fashion for selection of regions of interest or in a top-down taskpriming fashion. To these well-known attentional mechanisms, we add here an additional novel one. The approach is inspired(More)
In prior work, we put forth a model of visual saliency motivated by information theoretic considerations [1]. In this effort we consider how this proposal extends to explain saliency in the spatiotemporal domain and further, propose a distributed representation for visual saliency comprised of localized hierarchical saliency computation. Evidence for the(More)